Biochemistry, Biophysics, and Structural Biology Commons^™

Expression Changes Confirm Genomic Variants Predicted To Result In Allele-Specific, Alternative Mrna Splicing, Peter Rogan

Biochemistry Publications

Splice isoform structure and abundance can be affected by either noncoding or masquerading coding variants that alter the structure or abundance of transcripts. When these variants are common in the population, these nonconstitutive transcripts are sufficiently frequent so as to resemble naturally occurring, alternative mRNA splicing. Prediction of the effects of such variants has been shown to be accurate using information theory-based methods. Single nucleotide polymorphisms (SNPs) predicted to significantly alter natural and/or cryptic splice site strength were shown to affect gene expression. Splicing changes for known SNP genotypes were confirmed in HapMap lymphoblastoid cell lines with gene expression microarrays …

Transcription Factor Binding Site Clusters Identify Target Genes With Similar Tissue-Wide Expression And Buffer Against Mutations., Peter Rogan, Ruipeng Lu

Biochemistry Publications

Background: The distribution and composition of cis-regulatory modules composed of transcription factor (TF) binding site (TFBS) clusters in promoters substantially determine gene expression patterns and TF targets. TF knockdown experiments have revealed that TF binding profiles and gene expression levels are correlated. We use TFBS features within accessible promoter intervals to predict genes with similar tissue-wide expression patterns and TF targets using Machine Learning (ML). Methods: Bray-Curtis Similarity was used to identify genes with correlated expression patterns across 53 tissues. TF targets from knockdown experiments were also analyzed by this approach to set up the ML framework. TFBSs were …

A Unified Analytic Framework For Prioritization Of Non-Coding Variants Of Uncertain Significance In Heritable Breast And Ovarian Cancer, Eliseos J. Mucaki, Natasha G. Caminsky, Ami M. Perri, Ruipeng Lu, Alain Laederach, Matthew Halvorsen, Joan H. M. Knoll, Peter K. Rogan

Biochemistry Publications

Background

Sequencing of both healthy and disease singletons yields many novel and low frequency variants of uncertain significance (VUS). Complete gene and genome sequencing by next generation sequencing (NGS) significantly increases the number of VUS detected. While prior studies have emphasized protein coding variants, non-coding sequence variants have also been proven to significantly contribute to high penetrance disorders, such as hereditary breast and ovarian cancer (HBOC). We present a strategy for analyzing different functional classes of non-coding variants based on information theory (IT) and prioritizing patients with large intragenic deletions.

Methods

We captured and enriched for coding and non-coding variants …

A Unified Framework For The Prioritization Of Variants Of Uncertain Significance In Hereditary Breast And Ovarian Cancer Patients, Natasha G. Caminsky

Electronic Thesis and Dissertation Repository

A significant proportion of hereditary breast and ovarian cancer (HBOC) patients receive uninformative genetic testing results, an issue exacerbated by the overwhelming quantity of variants of uncertain significance identified. This thesis describes a framework where, aside from protein coding changes, information theory (IT)-based sequence analysis identifies and prioritizes pathogenic variants occurring within sequence elements predicted to be recognized by proteins involved in mRNA splicing, transcription, and untranslated region binding and structure. To support the utilization of IT analysis, we established IT-based variant interpretation accuracy by performing a comprehensive review of mutations altering mRNA splicing in rare and common diseases.

Custom …

Validation Of Predicted Mrna Splicing Mutations Using High-Throughput Transcriptome Data, Coby Viner, Stephanie Dorman, Ben Shirley, Peter Rogan

Biochemistry Publications

Interpretation of variants present in complete genomes or exomes reveals numerous sequence changes, only a fraction of which are likely to be pathogenic. Mutations have been traditionally inferred from allele frequencies and inheritance patterns in such data. Variants predicted to alter mRNA splicing can be validated by manual inspection of transcriptome sequencing data, however this approach is intractable for large datasets. These abnormal mRNA splicing patterns are characterized by reads demonstrating either exon skipping, cryptic splice site use, and high levels of intron inclusion, or combinations of these properties. We present, Veridical, an in silico method for the automatic validation …